Continue
FreeOpen-source AI code assistant for VS Code and JetBrains. Autocomplete, chat, and edit code with any LLM — local or cloud — fully customizable.
What does this tool do?
Continue is an open-source AI code assistant that integrates directly into VS Code and JetBrains IDEs, functioning as a local-first alternative to GitHub Copilot. It provides three core capabilities: code autocomplete for real-time suggestions, an in-editor chat interface for asking questions about code, and an edit mode for AI-assisted refactoring. The distinguishing feature is flexibility in LLM selection—users can connect any cloud-based model (OpenAI, Claude, Gemini) or run entirely local models for privacy-conscious teams. It's fully configurable through configuration files, allowing organizations to specify which models to use, temperature settings, and custom prompts. The tool positions itself against generic AI assistants by enabling standards enforcement through version-controlled checks that run as AI agents on pull requests, automating repetitive code review work while respecting team-defined standards rather than imposing opinionated defaults.
AI analysis from Feb 23, 2026
Key Features
- IDE-native autocomplete providing real-time code suggestions while typing in VS Code and JetBrains
- In-editor chat for asking questions about selected code without leaving the development environment
- AI-powered code editing and refactoring with multi-line edit capabilities
- Pull request automation through AI agents that enforce version-controlled standards defined in markdown files
- Multi-LLM support allowing configuration of different models for different tasks (completion vs. chat vs. review)
- Local model execution capability for privacy and offline operation
Use Cases
- 1Teams wanting privacy-first code assistance by running local LLMs instead of sending code to cloud providers
- 2Organizations enforcing custom coding standards and style guides through automated AI checks on every pull request
- 3Engineering teams reducing time spent on mechanical first-pass code reviews by automating detection of describable issues
- 4Individual developers seeking IDE-integrated autocomplete without switching between tools or losing focus
- 5Companies with multiple LLM preferences standardizing on a single interface that supports Claude, GPT-4, local models simultaneously
- 6Development teams building custom AI workflows through configuration rather than waiting for feature releases
Pros & Cons
Advantages
- LLM agnostic architecture eliminates vendor lock-in and allows teams to switch between OpenAI, Anthropic, local models, or on-premise solutions without changing tools
- Source-controlled standards enforcement means code quality rules are reviewable, versioned, and owned by teams rather than imposed by black-box algorithms
- Local model support enables zero-telemetry operation for security-conscious organizations while maintaining full IDE integration
- Configuration-driven approach lets teams customize behavior through markdown/config files without forking the project
Limitations
- Requires technical setup to configure custom LLM endpoints and standards—significantly more friction than plug-and-play alternatives like Copilot
- No pricing information publicly available; unclear monetization model or whether enterprise support/guarantees exist for production use
- Local model performance heavily depends on available compute; smaller models may produce lower-quality suggestions compared to cloud-based GPT-4 alternatives
- Fragmented user base between code completion users and pull request CI/CD users creates potential confusion about core value proposition
Pricing Details
Pricing details not publicly available.
Who is this for?
Engineering teams and individual developers prioritizing data privacy, customization, or avoiding vendor lock-in; organizations with strict code standards wanting to automate enforcement; companies evaluating multiple LLM providers; teams building custom AI workflows into CI/CD pipelines rather than relying on IDE-only assistants.